The present disclosure relates to predicting arrival times of vehicles in a public transportation system, such as a public bus, train or plane system. More specifically, the present disclosure relates to modeling dependency graphs to predict the arrival times of the vehicles.
Public transportation is a crucial element of most cities and towns all around the world. It is generally a safe, cheap and a sustainable mode of transportation for large numbers of people. However, adoption and utilization of public transportation by the general public typically depends on the quality of service being provided.
Many service providers monitor and analyze analytics related to the services they provide. For example, computer aided dispatch/automated vehicle location (CAD/AVL) is a system in which public transportation vehicle positions are determined through a global positioning system (GPS) and transmitted to a central server located at a transit agency's operations center and stored in a database for later use. The CAD/AVL system also typically includes two-way radio communication by which a transit system operator can communicate with vehicle drivers. The CAD/AVL system may further log and transmit incident information including an event identifier (ID) and a time stamp related to various events that occur during operation of the vehicle. For example, for a public bus system, logged incidents can include door opening and closing, driver logging on or off, wheel chair lift usage, bike rack usage, current bus condition, and other similar events. Some incidents are automatically logged by the system as they are received from vehicle on-board diagnostic systems or other data collection devices, while others are entered into the system manually by the operator of the vehicle.
For a typical public transportation company, service reliability is defined as variability of service attributes. Problems with reliability are ascribed to inherent variability in the system, especially demand for transit, operator performance, traffic, weather, road construction, crashes, and other similar unavoidable or unforeseen events. As transportation providers cannot control all aspects of operation owing to these random and unpredictable disturbances, they must adjust to the disturbances to maximize reliability. Several components that determine reliable service are schedule adherence, maintenance of uniform headways (e.g., the time between vehicles arriving in a transportation system), minimal variance of maximum passenger loads, and overall trip times. However, most public transportation companies put a greater importance on schedule adherence, including predicted arrival times of a vehicle at a specific stop.
Research related to travel time prediction is generally classified into two categories: (1) dynamically tracking a vehicle to predict its likely arrival time at a specific stop; and (2) using historical information to map a set of static features (such as time of day, bus stop location, route information) to calculate arrival time at a specific stop. However, both of these approaches have various drawbacks. Approach 1 relies heavily on the real-time tracking signal (e.g., the GPS location of a vehicle) and is greatly impacted by problems with the tracking signal such as loss of signal during operation of the vehicle. Additionally, approach 1 is directed to real-time modeling of the movement of the vehicle, and is more highly effective for short range predictions.
In approach 2, the models used are typically based upon static features such as historical operating parameters of a vehicle or a specific route within the transportation system. This results in a prediction that is typically accurate for long range time predictions, but it cannot quickly adapt to changing dynamics within the transportation system that can result in unexpected delays or changes to arrival times.